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Cluster Analysis by Mixture Identification

  • Chapter
Book cover Data Analysis in Astronomy

Part of the book series: Ettore Majorana International Science Series ((EMISS,volume 24))

Abstract

Clusters analysis is frequently defined as the problem of partitioning a collection of objects into groups of similar objects according to some numerical measure of similarity. A wide variety of methods for doing this have been available for quite some time. The field has been—and remains—very well documented in the open literature [see, e.g., the books by Anderberg (1973), Diday (1979), Jambu (1978), Jambu and Lebeau (1983), Sokal and Sneath (1963), and the surveys by Diday and Simon (1976), Duda and Hart (1973), Redner and Walker (1984), to cite a few]. However, permanent emergence of new technical requirements as well as recognition of the limitations of existing techniques continue fostering intensive research activity worldwide which is mirrored by a permanent flow of publications proposing new ideas and algorithms.

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Author information

Authors and Affiliations

  1. Philips Research Laboratory, Ave. Em. Van Becelaere 2, Box 8, Brussels, Belgium

    Pierre A. Devijver

Authors
  1. Pierre A. Devijver
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Editor information

Editors and Affiliations

  1. Institute of Cosmic Physics and Informatics/CNR, University of Palermo, Palermo, Italy

    V. Di Gesù  & L. Scarsi  & 

  2. European Southern Observatory, Garching/Munich, Federal Republic of Germany

    P. Crane

  3. Stanford University, Stanford, California, USA

    J. H. Friedman

  4. University of Rome, Rome, Italy

    S. Levialdi

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© 1985 Plenum Press, New York

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Devijver, P.A. (1985). Cluster Analysis by Mixture Identification. In: Gesù, V.D., Scarsi, L., Crane, P., Friedman, J.H., Levialdi, S. (eds) Data Analysis in Astronomy. Ettore Majorana International Science Series, vol 24. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-9433-8_3

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  • DOI: https://doi.org/10.1007/978-1-4615-9433-8_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4615-9435-2

  • Online ISBN: 978-1-4615-9433-8

  • eBook Packages: Springer Book Archive

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